The article starts by criticizing REST and offering their toolkit as the solution. What’s really funny, is that the problems they ding are not RESTful issues.

REST requires lots of hops

Let’s start with this one:

As you might notice, this is less than ideal. When all is said and done we have made 1 + M + M + sum(Am) round trip calls to our API where M is the number of movies and sum(Am) is the sum of the number of acting credits in each of the M movies. For applications with small data requirements, this might be okay but it would never fly in a large, production system.

Conclusion? Our simple RESTful approach is not adequate. To improve our API, we might go ask someone on the backend team to build us a special /moviesAndActors endpoint to power this page. Once that endpoint is ready, we can replace our 1 + M + M + sum(Am) network calls with a single request.

This is the classic problem when you run into when fetching a 3NF (3rd normal form) data structure served up as REST.

Tip #1: REST doesn’t prevent you from merging data or offering previews of combined data. Formats like HAL include ability to serve up _embedded data, letting you give clients what they need. Spring Data REST does this through projections, but you can use anything.

In fact, server-side providers will probably have a better insight into exactly the volume of traffic fetching such data before clients. And through the power of hypermedia, can evolve to add links to the hypermedia without breaking existing clients. Old clients can do multiple hops; new clients can proceed to consume the new links, with full backwards compatibility.

REST serves too much data

If you look closely, you’ll notice that our page is using a movie’s title and image, and an actor’s name and image (i.e. we are only using 2 of 8 fields in a movie object and 2 of 7 fields in an actor object). That means we are wasting roughly three-quarters of the information that we are requesting over the network! This excess bandwidth usage can have very real impacts on performance as well as your infrastructure costs!

Just a second ago, we complained that the REST API was serving up too little data, forcing us to take multiple hops. Now we are complaining that it serves too much data and is wasting bandwidth.

The example in the article is a bit forced, given we are probably talking a couple tweets worth of data. It’s not like they are shipping 50MB too much. In fact, big volume data (images, PDFs) would best be served as links in the hypermedia. This would let the browser efficiently fetch a linked item once, and lean on the browser’s cache.

But I sense the real derision in the article is because the endpoint isn’t tailored to the client’s precise demands. No, the real example here is to illustrate a query technique on the client.

Just put SQL in the client already!

Wouldn’t it be nice if we could build a generic API that explicitly represents the entities in our data model as well as the relationships between those entities but that does not suffer from the 1 + M + M + sum(Am) performance problem? Good news! We can!

With GraphQL, we can skip directly to the optimal query and fetch all the info we need and nothing more with a simple, intuitive query.

So now we get to the real intent of the article: introduce a query language. Presumably solving REST’s straw man “problems” (which it doesn’t).

2/ if you want to query the database, why go there? Instead, just open a SQL connection straight to the DB.

If you want to write a highly detailed query, just open a connection the data store and query directly. That’s what query languages are for. Why invent something that’s weblike, but really just Another Query Language?

What problem are you solving?

GraphQL takes a fundamentally different approach to APIs than REST. Instead of relying on HTTP constructs like verbs and URIs, it layers an intuitive query language and powerful type system on top of our data. The type system provides a strongly-typed contract between the client and server, and the query language provides a mechanism that the client developer can use to performantly fetch any data he or she might need for any given page.

This query technology may be quite handy if you must write intense, focused queries. If cutting a couple text-based columns makes that much difference, then REST may not be solution you seek. (Of course, at that point why not just have your JavaScript frontend open a SQL/MongoDB/Neo4j connection?)

What does REST solve? REST solves the brittle problem that arose with CORBA and SOAP.

REST makes it possible to evolve APIs without forcing you to update every client at once.

Think about that. When web sites make updates, does the web browser require an update? And why?

It’s no light feat of accomplishment. People were being beaten up left right as APIs would evolve. Updates were tough. Some clients would get broken. And availability is key for web scale business. So adopting the tactics that made the web resilient into API design sounds like a keen idea to try.

Too bad not enough people actually KNOW what these concepts are, and press on to criticize REST while offering “fixes” that don’t even address its fundamentals. The solution served in the article would put strong domain knowledge into the client, resulting in tight coupling. REST doesn’t shoot for this.

Am I making this assessment up?

This “virtual graph” is more explicitly expressed as a schema. A schema is a collection of types, interfaces, enums, and unions that make up your API’s data model. GraphQL even includes a convenient schema language that we can use to define our API.